Influencing factors of pathologic complete response after neoadjuvant therapy in human epidermal growth factor receptor 2 positive breast cancer patients
Objective To investigate the influencing factors of pathologic complete response(pCR)after neoadju-vant therapy in human epidermal growth factor receptor 2(HER2)positive breast cancer,and to establish nomogram pre-diction model.Method A total of 171 patients with HER2 positive breast cancer who received neoadjuvant therapy were divided into pCR group and non-pCR group according to postoperative pathological results,and the influencing fac-tors of pCR were obtained by univariate and multivariate Logistic regression analysis,and the nomogram prediction model was constructed.The predictive ability of the model was evaluated and internally validated using receiver operat-ing characteristic(ROC)curve and Hosmer-Lemeshow test.Result Among 171 patients with HER2 positive breast can-cer,110 patients achieved pCR,the pCR rate was 64.3%.There were significant differences in hormone receptor(HR)status and HER2 status between pCR and non-PCR HER2 positive breast cancer patients(P<0.01).The univariate analy-sis showed that HR status,HER2 status and Ki-67 were influencing factors of pCR after neoadjuvant therapy(P<0.05).A nomogram prediction model was constructed based on the above factors,and the area under the curve was 0.707(95%CI:0.629-0.784),which showed that the nomogram prediction model had good differentiation.The results of the Hosmer-Lemeshow test was χ2=0.054,P=1,which showed that the pCR predicted by the nomogram prediction model was in good agreement with the actual occurrence probability,indicating that the nomogram prediction model had good calibration degree.Conclusion HR status,HER2 status,Ki-67 are independent influencing factors of pCR after neoad-juvant therapy in HER2 positive breast cancer.The nomogram predictive model has good effect,and can be used as a tool to predict pCR after neoadjuvant therapy in HER2 positive breast cancer,which is helpful to guide clinical individu-alized treatment strategies.
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